3-D volume imaging can be a diagnostic tool that offers advantages over earlier 2-D radiographic imaging techniques for evaluating the condition of internal structures and organs. 3-D imaging of a patient or other subject has been made possible by a number of advancements, including the development of high-speed imaging detectors, such as digital radiography (DR) detectors that enable multiple images to be taken in rapid succession.
Cone beam computed tomography (CBCT) or cone beam CT technology offers considerable promise as one type of diagnostic tool for providing 3-D volume images. Cone beam CT systems capture volume data sets by using a high frame rate flat panel digital radiography (DR) detector and an x-ray source, typically affixed to a gantry that revolves about the object to be imaged, directing, from various points along its orbit around the subject, a divergent cone beam of x-rays toward the subject. The CBCT system captures projection images throughout the source-detector orbit, for example, with one 2-D projection image at every degree increment of rotation. The projections are then reconstructed into a 3-D volume image using various algorithmic techniques. Among the methods for reconstructing the 3-D volume image are filtered back projection (FBP) approaches. An exemplary reconstruction approach is described, for example, in the paper by L. A. Feldkamp, L. C. Davis, and J. W. Kress, entitled “Practical cone-beam algorithm,” Journal of the Optical Society of America, vol 1, pp. 612-619, June, 1984.
Although 3-D images of diagnostic quality can be generated using CBCT systems and technology, technical challenges remain. Highly dense objects, such as metallic implants, prostheses and related appliances, surgical clips and staples, dental fillings, and the like can cause various image artifacts that can obscure useful information about the imaged tissue. This occurs because dense objects having a high atomic number attenuate X-rays in the diagnostic energy range much more strongly than do soft tissue or bone features. When dense structures are in the exposure path, fewer photons reach the imaging detector through these objects. For 3-D imaging, the image artifacts that can be generated in reconstruction routines by metallic and other highly dense objects include dark and/or bright streaks that spread across the entire reconstructed image. Such artifacts can be due to physical effects such as high quantum noise, radiation scatter, beam hardening, and/or non-linear amplification in reconstruction algorithms. These artifacts, generically referred to metallic artifacts or metal artifacts, can reduce image quality by masking soft tissue structures, not only in the immediate vicinity of the dense object, but also throughout the entire image. Without some type of compensation, metal artifacts in 3-D volumes can falsify CT values and even make it difficult or impossible to use the reconstructed image effectively in assessing patient condition or properly planning radiation therapy or other treatments.
Approaches have been tried for metal artifacts reduction (MAR), including: 1. Interpolation-based FBP reconstruction approach; 2. Iterative reconstruction approach; and 3. Quasi-iterative based FBP approach.
An exemplary MAR approach is described, for example, by W. A. Kalender, R. Hebele, and J. Ebersberger, in an article entitled “Reduction of CT artifacts caused by metallic implants”, Radiology 164(2), 576{577 (1987).
It is recognized that metal artifacts reduction is a challenging task, particularly where implant geometries may be more complex. There is a need for metal artifacts reduction that offer improved performance and/or computational efficiency.